| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | legacy_learning_rate_decay.py | 15 """Various learning rate decay functions."""
35 """Applies exponential decay to the learning rate.
37 When training a model, it is often recommended to lower the learning rate as
39 to a provided initial learning rate. It requires a `global_step` value to
40 compute the decayed learning rate. You can just pass a TensorFlow variable
43 The function returns the decayed learning rate. It is computed as:
51 integer division and the decayed learning rate follows a staircase function.
71 The initial learning rate.
78 staircase: Boolean. If `True` decay the learning rate at discrete intervals
84 learning rate.
[all …]
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| D | learning_rate_schedule.py | 15 """Various learning rate decay functions."""
33 """The learning rate schedule base class.
35 You can use a learning rate schedule to modulate how the learning rate
38 Several built-in learning rate schedules are available, such as
77 raise NotImplementedError("Learning rate schedule must override __call__")
81 raise NotImplementedError("Learning rate schedule must override get_config")
100 When training a model, it is often useful to lower the learning rate as
102 to an optimizer step, given a provided initial learning rate.
104 The schedule a 1-arg callable that produces a decayed learning
106 the learning rate value across different invocations of optimizer functions.
[all …]
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| D | adadelta.py | 33 adaptive learning rate per dimension to address two drawbacks:
35 - The continual decay of learning rates throughout training.
36 - The need for a manually selected global learning rate.
38 Adadelta is a more robust extension of Adagrad that adapts learning rates
40 past gradients. This way, Adadelta continues learning even when many updates
42 don't have to set an initial learning rate. In this version, the initial
43 learning rate can be set, as in most other Keras optimizers.
46 learning_rate: Initial value for the learning rate:
50 Note that `Adadelta` tends to benefit from higher initial learning rate
|
| /external/tensorflow/tensorflow/core/protobuf/tpu/ |
| D | optimization_parameters.proto | 37 // Dynamic learning rate specification in the TPUEmbeddingConfiguration. The
38 // actual learning rates are provided as a scalar input list to the
42 // For tables where learning rates are dynamically computed and communicated
43 // to the TPU embedding program, a tag must be specified for the learning
49 // learning rate, and specifies exactly one tag if it uses dynamic learning
57 // the same dynamic learning rate, for example, their dynamic learning rate
63 // communicate dynamic learning rates to the TPU embedding program.
65 // equal to the number of unique tags. The learning rate associated with a
71 // Source of learning rate to use.
121 // computing the effective learning rate. When update_accumulator_first is set
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_embedding_v2_utils.py | 215 a learning rate of 0.2 while the second feature will be looked up in a table
216 that has a learning rate of 0.1.
234 learning_rate: The learning rate. It should be a floating point value or a
235 callable taking no arguments for a dynamic learning rate.
244 `weight_decay_factor` is multiplied by the current learning rate.
316 a learning rate of 0.2 while the second feature will be looked up in a table
317 that has a learning rate of 0.1.
338 learning_rate: The learning rate. It should be a floating point value or a
339 callable taking no arguments for a dynamic learning rate.
348 `weight_decay_factor` is multiplied by the current learning rate.
[all …]
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| /external/tensorflow/tensorflow/lite/g3doc/android/ |
| D | index.md | 3 TensorFlow Lite lets you run TensorFlow machine learning (ML) models in your
8 ## Learning roadmap {:.hide-from-toc}
53 ## Machine learning models
56 portable, more efficient machine learning model format. You can use pre-built
64 This page discusses using already-built machine learning models and does not
66 picking, modifying, building, and converting machine learning models for
132 learning models into your Android app:
143 for performing common machine learning tasks on handling visual, audio, and
201 called *accelerators*. Machine learning models can run faster on these
233 you have a machine learning model that uses ML operations that are not supported
[all …]
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| D | quickstart.md | 4 to analyze a live camera feed and identify objects using a machine learning
10 ## Object detection with machine learning
13 The machine learning model in this tutorial performs object detection. An object
19 size of data being processed, and the size of the machine learning model.
105 TensorFlow Lite machine learning models, and access utility functions that
110 of the object detection machine learning model:
113 classes, execution of the machine learning model, and output results from
118 data object that can be processed by the machine learning model.
149 In your Android app, you must initialize the TensorFlow Lite machine learning
203 of machine learning models using specialized processing hardware on a mobile
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| /external/tensorflow/tensorflow/core/tfrt/common/ |
| D | BUILD | 12 # copybara:uncomment "//learning/brain/experimental/dtensor/...",
13 # copybara:uncomment "//learning/brain/experimental/tfrt/...",
14 # copybara:uncomment "//learning/brain/google/xla/...",
15 # copybara:uncomment "//learning/brain/tfrc/...",
16 # copybara:uncomment "//learning/brain/tfrt/...",
|
| /external/tensorflow/tensorflow/python/keras/ |
| D | optimizer_v1.py | 165 learning rate decay, and Nesterov momentum.
168 lr: float >= 0. Learning rate.
171 decay: float >= 0. Learning rate decay over each update.
236 (except the learning rate, which can be freely tuned).
239 lr: float >= 0. Learning rate.
243 decay: float >= 0. Learning rate decay over each update.
305 Adagrad is an optimizer with parameter-specific learning rates,
314 lr: float >= 0. Initial learning rate.
316 decay: float >= 0. Learning rate decay over each update.
319 - [Adaptive Subgradient Methods for Online Learning and Stochastic
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| /external/tensorflow/tensorflow/lite/g3doc/examples/build/ |
| D | index.md | 5 Lite model format. The machine learning (ML) models you use with TensorFlow
41 with a machine learning model is limited on a mobile or edge device. Models
46 support a subset of machine learning model operations compared to
71 machine learning (ML) models for vision and natural language processing (NLP).
73 models on standard datasets. The machine learning models in the
100 the machine learning workflow. It enables tracking experiment metrics like
112 model performance well and uses less compute resources. Machine learning model
|
| /external/tensorflow/tensorflow/compiler/mlir/tfrt/ |
| D | BUILD | 31 "//learning/brain/experimental/mlir/tflite/tfmrt/...",
32 "//learning/brain/experimental/mlir/tfrt_compiler/...",
33 "//learning/brain/experimental/tfrt/...",
34 "//learning/brain/tfrt/...",
35 "//learning/infra/mira/...",
36 "//learning/serving/contrib/tfrt/mlir/...",
38 "//learning/brain/mlir/mlir_lsp_server/...",
426 "//learning/brain/tfrt/tpu/compiler/mlir:tf_to_tfrt_tpu",
536 "//learning/brain/tfrt/tpu/compiler/mlir:tf_to_tfrt_tpu",
549 # copybara:uncomment "//learning/brain/experimental/tfrt/visualization:__pkg__",
[all …]
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| /external/tensorflow/tensorflow/python/training/ |
| D | proximal_adagrad.py | 31 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:
34 Efficient Learning using Forward-Backward Splitting:
35 …[Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-sp…
36 … ([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
45 learning_rate: A `Tensor` or a floating point value. The learning rate.
|
| D | proximal_gradient_descent.py | 32 Efficient Learning using Forward-Backward Splitting:
33 …[Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-sp…
34 … ([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
43 learning_rate: A Tensor or a floating point value. The learning
|
| /external/tensorflow/tensorflow/lite/g3doc/examples/audio_classification/ |
| D | overview.md | 66 Learning, Web and Mobile deployment. It also has a more complex output.
120 You can use a technique known as transfer learning to re-train a model to
127 custom dataset, in a few lines of codes. It uses transfer learning to reduce the
129 [Transfer learning for audio recognition](https://www.tensorflow.org/tutorials/audio/transfer_learn…
130 as an example of transfer learning.
138 * [Transfer learning for audio recognition](https://www.tensorflow.org/tutorials/audio/transfer_l…
|
| /external/iproute2/ip/ |
| D | iplink_vxlan.c | 38 " [ [no]learning ]\n" in print_explain()
82 __u8 learning = 1; in vxlan_parse_opt() local
246 learning = 0; in vxlan_parse_opt()
247 } else if (!matches(*argv, "learning")) { in vxlan_parse_opt()
249 learning = 1; in vxlan_parse_opt()
316 learning = 0; in vxlan_parse_opt()
317 /* we will add LEARNING attribute outside of the loop */ in vxlan_parse_opt()
385 addattr8(n, 1024, IFLA_VXLAN_LEARNING, learning); in vxlan_parse_opt()
500 __u8 learning = rta_getattr_u8(tb[IFLA_VXLAN_LEARNING]); in vxlan_print_opt() local
502 print_bool(PRINT_JSON, "learning", NULL, learning); in vxlan_print_opt()
[all …]
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| /external/tensorflow/tensorflow/lite/g3doc/android/tutorials/ |
| D | audio_classification.md | 4 learning models to recognize sounds and spoken words in an Android app. Audio
15 information on how audio is pre-processed for use with machine learning models,
19 ## Audio classification with machine learning
21 The machine learning model in this tutorial recognizes sounds or words from
99 TensorFlow Lite machine learning models, and access utility functions that
107 learning model, and output results from the model processing.
128 In your Android app, you must initialize the TensorFlow Lite machine learning
179 modules that accelerate execution of machine learning models using specialized
323 processing Fragment or Activity loses focus. Running a machine learning model
399 information about implementing machine learning in your mobile application with
|
| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_SendTPUEmbeddingGradients.pbtxt | 18 A TensorList of float32 scalars, one for each dynamic learning
21 Multiple tables can share the same dynamic learning rate tag as specified
22 in the configuration. If the learning rates for all tables are constant,
|
| /external/tensorflow/tensorflow/lite/g3doc/examples/ |
| D | _index.yaml | 17 learning (ML) model format. You can use pre-trained models with TensorFlow Lite, modify
27 <h2 class="tfo-landing-page-heading no-link">Learning roadmap</h2>
50 You don't have to build a TensorFlow Lite model to start using machine learning on
56 <li>Start developing machine learning features with already
69 learning, you should review the use cases supported by
|
| /external/tensorflow/tensorflow/core/tfrt/run_handler_thread_pool/ |
| D | BUILD | 13 # copybara:uncomment "//learning/infra/mira/...",
14 # copybara:uncomment "//learning/serving/...",
15 # copybara:uncomment "//learning/brain/experimental/tfrt/...",
16 # copybara:uncomment "//learning/brain/tfrt/tfrt_session/...",
|
| /external/tensorflow/tensorflow/core/runtime_fallback/ |
| D | BUILD | 9 "//learning/brain/experimental/mlir/tflite/tfmrt/...",
10 "//learning/brain/experimental/tfrt/...",
11 "//learning/brain/tfrt/...",
12 "//learning/brain/mobile/lite/...",
|
| /external/iproute2/bridge/ |
| D | link.c | 23 [BR_STATE_LEARNING] = "learning",
190 print_onoff(fp, "learning", in print_linkinfo()
237 fprintf(stderr, " [ learning {on | off} ]\n"); in usage()
276 __s8 learning = -1; in brlink_modify() local
311 } else if (strcmp(*argv, "learning") == 0) { in brlink_modify()
313 if (!on_off("learning", &learning, *argv)) in brlink_modify()
402 if (learning >= 0) in brlink_modify()
403 addattr8(&req.n, sizeof(req), IFLA_BRPORT_LEARNING, learning); in brlink_modify()
|
| /external/tensorflow/ |
| D | README.md | 19 for machine learning. It has a comprehensive, flexible ecosystem of
28 conduct machine learning and deep neural networks research. The system is
153 * [Deep Learning with Tensorflow from Edx](https://www.edx.org/course/deep-learning-with-tensorfl…
158 * [TensorFlow 2 for Deep Learning Specialization from Coursera](https://www.coursera.org/speciali…
160 * [Machine Learning with TensorFlow on GCP from Coursera](https://www.coursera.org/specialization…
161 … [Intro to TensorFlow for Deep Learning from Udacity](https://www.udacity.com/course/intro-to-tens…
|
| /external/tensorflow/tensorflow/core/tfrt/saved_model/ |
| D | BUILD | 12 # copybara:uncomment "//learning/brain/experimental/tfrt/...",
13 # copybara:uncomment "//learning/brain/tfrt/...",
14 # copybara:uncomment "//learning/infra/mira/...",
15 # copybara:uncomment "//learning/serving/...",
|
| /external/tensorflow/tensorflow/compiler/mlir/tfrt/ir/ |
| D | BUILD | 16 # copybara:uncomment "//learning/brain/experimental/tfrt:__subpackages__",
17 # copybara:uncomment "//learning/brain/tfrt/tpu/compiler:__subpackages__",
35 # copybara:uncomment "//learning/brain/experimental/tfrt:__subpackages__",
115 "//learning/brain/tfrt/tpu/compiler:__subpackages__",
|
| /external/tensorflow/tensorflow/tools/dockerfiles/partials/onednn/redhat/ |
| D | cpu.partial.Dockerfile | 10 …rformance library for deep learning applications. TensorFlow* is a widely-used machine learning fr…
|